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AI features in Cube use a token-based system to measure and manage consumption.

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Overview

Cube’s AI-powered features consume tokens based on the resources required to complete each request. Token allocation differs by customer type:

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Token consumption

Token usage depends on several factors:
  • Task complexity — More complex questions, multi-step analysis, or larger datasets consume more tokens than simple lookups. Each message in a session carries prior context, so longer sessions compound usage.
  • Data model context — Cube sends context from your data model to the LLM to improve answer accuracy. Larger models with more fields and descriptions use more tokens per request.
  • AI model — More capable models consume more tokens per request than lighter models.
Not all AI features consume tokens. The list of features that consume tokens is subject to change as the product evolves.

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Per-seat token grants

On-demand customers on paid plans receive per-seat token grants equal to half of the seat price. Each user is awarded an individual monthly token allocation based on their role. Per-seat grants:
  • Are assigned to the individual user
  • Reset each billing cycle
  • Cannot be transferred, shared, or rolled over

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On-demand consumption

When a user on an on-demand plan exceeds their monthly per-seat grant, usage automatically continues as on-demand consumption. On-demand usage is billed through the credit card on file. Administrators can set a monthly on-demand spending limit to control additional costs. This limit caps the total on-demand spend across the account for each billing cycle.

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Token packages

Contract customers can purchase pooled add-on token packages. Token packages are added to a shared pool accessible by all users in the account.
  • Each package is valid for the duration of the contract or until fully consumed, whichever comes first
  • Multiple active packages can be combined
  • Packages do not auto-renew
Contact your account executive for details on purchasing token packages.

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Free tier

Each user on a free plan receives an individual monthly token allowance. This allowance resets at the start of each calendar month, and usage is tracked against it in real time — once a user’s spend for the month reaches the allowance, further AI requests are blocked until the next reset (see When limits are reached). If an account downgrades to the free plan mid-month, that month’s already recorded spend is drawn against the new free allowance immediately, rather than resetting.

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Tracking usage

Administrators can monitor token consumption through the AI tab in the billing settings page, split into two views:
  • AI Usage — aggregate token consumption over time, remaining allocation from per-seat grants and token packages, and a breakdown by user, role, and usage dimension.
  • AI Requests — a raw, per-request log (with a live tail) for inspecting individual AI calls as they happen.

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When limits are reached

When a user exhausts all available token sources (per-seat grant and token packages), AI requests will return an error indicating the token limit has been exceeded.
  • Administrators are directed to the billing page to purchase additional token packages
  • Non-admin users are prompted to contact their account administrator to increase token quotas

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FAQ

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Do tokens roll over?

  • Per-seat grants reset each billing cycle and do not roll over
  • Token packages remain active for the duration of the contract

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Can I bring my own AI model?

Yes. When using a Bring Your Own Model (BYOM) configuration, AI requests bypass the token quota system entirely — no tokens are consumed or tracked for those requests. You are billed directly by your model provider.

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Why does a single prompt sometimes use more tokens than expected?

Cube’s AI features use an agentic architecture. A single prompt may trigger multiple internal steps — such as searching the data model, building a query, and summarizing results — each of which consumes tokens independently.

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Why does the same question use different amounts of tokens?

Token usage can vary between identical prompts due to differences in conversation context (earlier messages in the session) or the AI choosing a different reasoning path.